Stanford Seminar – Intelligent Coordination for Sustainable Roadways


April 28, 2023
Cathy Wu of MIT

Intelligent Coordination for Sustainable Roadways – If Autonomous Vehicles are the Answer, then What is the Question?

For all its hype, autonomous vehicles have yet to make our roadways more sustainable: safer, cheaper, cleaner. This talk suggests that key to unlocking sustainable roadways is to shift the focus from autonomy-driven design to use-driven design. Based on recent work, the talk focuses on three critical priorities––safety, cost, and environment––each leveraging the ‘autonomy’ capability of coordinating vehicles. But fully autonomous agents are not the only entities that can coordinate. A paragon of safety is air traffic control, in which expert operators remotely coordinate aircraft. The work brings these ideas to the dense traffic on roadways and analyzes the scalability of operators. Another much cheaper way to coordinate is to give a smartphone app to drivers. The work characterizes how well lower-tech systems can still achieve autonomous capabilities. For cleaner roadways, dozens of articles have considered coordinating vehicles to reduce emissions. This work models whether doing so would move the needle on climate change mitigation goals. To study these multi-agent coordination problems, the work leverages queueing theory, Lyapunov stability analysis, transfer learning, and multi-task reinforcement learning. The talk will also substantiate issues of robustness that arise when applying learning-based techniques and a new line of work designed to address them. Overall, the results indicate promise for intelligent coordination to enable sustainable roadways.

About the speaker:
Cathy Wu works at the intersection of machine learning, optimization, and large-scale urban systems and other societal systems. Her recent research focuses on mixed autonomy systems in mobility, which studies the complex integration of automation such as self-driving cars into existing urban systems. She is broadly interested in developing principled computational tools to enable reliable and complex decision-making for critical societal systems. Learn more:



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